scholarly journals Self-Attention based model forde-novoantibiotic resistant gene classification with enhanced reliability for Out of Distribution data detection

2019 ◽  
Author(s):  
Md-Nafiz Hamid ◽  
Iddo Friedberg
Keyword(s):  
Deep Learning ◽  
Optimization Method ◽  
Distribution Data ◽  
Data Detection ◽  
Antibiotic Resistant ◽  
Global Epidemic ◽  
Resistant Genes ◽  
Uncertainty Estimates ◽  
The Face ◽  
Deep Learning Model

AbstractAntibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Using traditional alignment based methods to detect antibiotic resistant genes results in huge number of false negatives. In this paper, we introduce a deep learning model based on a self-attention architecture that can classify antibiotic resistant genes into correct classes with high precision and recall by just using protein sequences as input. Additionally, deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against Out-of-Distribution (OoD) antibiotic resistant/non-resistant genes. We train our model with an optimization method called Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD) which provides reliable uncertainty estimates when tested against OoD data.

scholarly journals A Deep Neural Network for face Recognition

2019 ◽  
Vol 8 (12S) ◽  
pp. 420-423
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Facial Features ◽  
Biometric Authentication ◽  
Accuracy Rate ◽  
Learning Technique ◽  
The Face ◽  
Deep Learning Model

Face recognition is used to biometric authentication method to analyze the face extract and photographs useful to reputation formation from them, which can be usually called as a characteristic vector this is used to differentiate the organic features. In this paper to detect the suspect by extracting facial features from the captured image of the suspect from CCTV and match it with the pictures stored in the database and also to achieve an accuracy rate of 100 %, negligible loss using deep learning technique. For extracting the facial features, we are using deep learning model known as Convolutional Neural Network (CNN). It is one of the best models to extract features with the highest accuracy rate .

scholarly journals An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields

2020 ◽  
Author(s):  
Mert Sengul ◽  
Yao Song ◽  
Nadire Nayir ◽  
Yawei Gao ◽  
Ying Hung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Force Field ◽  
Interatomic Potential ◽  
Dynamical Evolution ◽  
Optimization Method ◽  
Learning Model ◽  
Chemical System ◽  
Latin Hypercube Design ◽  
Initial Design ◽  
Deep Learning Model

<div><div><div><p>ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method.</p></div></div></div>

scholarly journals An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields

2020 ◽  
Author(s):  
Mert Sengul ◽  
Nadire Nayir ◽  
Yawei Gao ◽  
Ying Hung ◽  
Tirthankar Dasgupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
Force Field ◽  
Interatomic Potential ◽  
Dynamical Evolution ◽  
Optimization Method ◽  
Learning Model ◽  
Chemical System ◽  
Latin Hypercube Design ◽  
Initial Design ◽  
Deep Learning Model

<div><div><div><p>ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method.</p></div></div></div>

Deep Learning Model for Face Recognition in Unconstrained Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4309-4312
Author(s):  
Rajeshwar Moghekar ◽  
Sachin Ahuja
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Transfer Learning ◽  
Face Database ◽  
Security Monitoring ◽  
Face Images ◽  
Feature Extractor ◽  
The Face ◽  
Deep Learning Model ◽  
Fine Tune

Face recognition from videos is challenging problem as the face image captured has variations in terms of pose, Occlusion, blur and resolution. It has many applications including security monitoring and authentication. A subset of Indian Movies Face database (IMFDB) which has collection of face images retrieved from movie/video of actors which vary in terms of blur, pose, noise and illumination is used in our work. Our work focuses on the use of pre-trained deep learning models and applies transfer learning to the features extracted from the CNN layers. Later we compare it Fine tuned model. The results show that the accuracy is 99.89 using CNN as feature extractor and 96.3 when we fine tune the VGG-Face. The Fine tuned network of VGG-Face learnt more generic features when compared with its counterpart transfer learning. When applied on VGG16 transfer learning achieved 93.9.

scholarly journals INDEEDopt: a deep learning-based ReaxFF parameterization framework

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mert Y. Sengul ◽  
Yao Song ◽  
Nadire Nayir ◽  
Yawei Gao ◽  
Ying Hung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Force Field ◽  
Optimization Method ◽  
Interatomic Potentials ◽  
Latin Hypercube Design ◽  
Nickel Chromium ◽  
Minimum Discrepancy ◽  
Conventional Optimization ◽  
Deep Learning Model

AbstractEmpirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.

scholarly journals An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields

2020 ◽  
Author(s):  
Mert Sengul ◽  
Yao Song ◽  
Nadire Nayir ◽  
Yawei Gao ◽  
Ying Hung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Force Field ◽  
Interatomic Potential ◽  
Dynamical Evolution ◽  
Optimization Method ◽  
Learning Model ◽  
Chemical System ◽  
Latin Hypercube Design ◽  
Initial Design ◽  
Deep Learning Model

<div><div><div><p>ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method.</p></div></div></div>

scholarly journals A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3898
Author(s):  
Duanguang Cao ◽  
Hanfa Xing ◽  
Man Sing Wong ◽  
Mei-Po Kwan ◽  
Huaqiao Xing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Optimization Method ◽  
Learning Model ◽  
Ensemble Model ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Basic Model ◽  
Building Information ◽  
Deep Learning Model

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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